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Accelerating drug discovery with Artificial: a whole-lab orchestration and scheduling system for self-driving labs

Published: April 1, 2025 | arXiv ID: 2504.00986v1

By: Yao Fehlis , Paul Mandel , Charles Crain and more

Potential Business Impact:

Finds new medicines faster with smart robots.

Business Areas:
Machine Learning Artificial Intelligence, Data and Analytics, Software

Self-driving labs are transforming drug discovery by enabling automated, AI-guided experimentation, but they face challenges in orchestrating complex workflows, integrating diverse instruments and AI models, and managing data efficiently. Artificial addresses these issues with a comprehensive orchestration and scheduling system that unifies lab operations, automates workflows, and integrates AI-driven decision-making. By incorporating AI/ML models like NVIDIA BioNeMo - which facilitates molecular interaction prediction and biomolecular analysis - Artificial enhances drug discovery and accelerates data-driven research. Through real-time coordination of instruments, robots, and personnel, the platform streamlines experiments, enhances reproducibility, and advances drug discovery.

Page Count
7 pages

Category
Computer Science:
Software Engineering